o * i @sddlmZddlZddlZddlmZddlZddlZddl m Z ddl m Z er>ddl mZddlmZddlmZmZGd d d e ZdS) ) annotationsN) TYPE_CHECKING) framework)TransformedDistribution)Sequence)Tensor) TransformUniformcseZdZUdZded<ded<ded<ded<d#fd d Zed$ddZed$ddZed$ddZ d%ddZ d%ddZ d%ddZ d$ddZ gfd&dd Zgfd&d!d"ZZS)'GumbelaThe Gumbel distribution with location `loc` and `scale` parameters. Mathematical details The probability density function (pdf) is .. math:: pdf(x; mu, sigma) = exp(-(x - mu) / sigma - exp(-(x - mu) / sigma)) / sigma In the above equation: * :math:`loc = \mu`: is the mean. * :math:`scale = \sigma`: is the std. Args: loc(int|float|tensor): The mean of gumbel distribution.The data type is int, float, tensor. scale(int|float|tensor): The std of gumbel distribution.The data type is int, float, tensor. Examples: .. code-block:: python >>> import paddle >>> from paddle.distribution.gumbel import Gumbel >>> # Gumbel distributed with loc=0, scale=1 >>> dist = Gumbel(paddle.full([1], 0.0), paddle.full([1], 1.0)) >>> # doctest: +SKIP >>> print(dist.sample([2])) Tensor(shape=[2, 1], dtype=float32, place=Place(cpu), stop_gradient=True, [[0.40484068], [3.19400501]]) >>> print(dist.rsample([2])) Tensor(shape=[2, 1], dtype=float32, place=Place(cpu), stop_gradient=True, [[-0.95093185], [ 0.32422572]]) >>> # doctest: -SKIP >>> value = paddle.full([1], 0.5) >>> print(dist.prob(value)) Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=True, [0.33070430]) >>> print(dist.log_prob(value)) Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=True, [-1.10653067]) >>> print(dist.cdf(value)) Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=True, [0.54523921]) >>> print(dist.entropy()) Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=True, [1.57721567]) rlocscaler base_distztuple[Transform, ...] transformsfloat | TensorreturnNonec st|tjtjtjjfstdt |t|tjtjtjjfs*tdt |t|tjr7tj d|d}t|tjrDtj d|d}|j |j krVt ||g\|_ |_n|||_ |_tjdd}tjt|j t|jt|j td|j|_d|_t|j|jdS)Nz5Expected type of loc is Real|Variable|Value, but got z7Expected type of scale is Real|Variable|Value, but got )shape fill_valueZfloat32)dtype) isinstancenumbersRealrVariablepaddleZpirValue TypeErrortypefullrZbroadcast_tensorsr r npfinfo distributionr Z full_likefloatZtinyZepsr rsuper__init__)selfr r r! __class__rf/home/app/PaddleOCR-VL-test/.venv_paddleocr/lib/python3.10/site-packages/paddle/distribution/gumbel.pyr%cs4      zGumbel.__init__cCs|j|jtjS)u`Mean of distribution The mean is .. math:: mean = \mu + \sigma * γ In the above equation: * :math:`loc = \mu`: is the location parameter. * :math:`scale = \sigma`: is the scale parameter. * :math:`γ`: is the euler's constant. Returns: Tensor: mean value. )r r r euler_gammar&rrr)meansz Gumbel.meancCs6tj|jjtjtj|jjd}t|jd|dS)aVariance of distribution. The variance is .. math:: variance = \sigma^2 * \pi^2 / 6 In the above equation: * :math:`scale = \sigma`: is the scale parameter. Returns: Tensor: The variance value. rrr) rrr rmathpir rpow)r&temprrr)variances  zGumbel.variancecCs t|jS)aStandard deviation of distribution The standard deviation is .. math:: stddev = \sqrt{\sigma^2 * \pi^2 / 6} In the above equation: * :math:`scale = \sigma`: is the scale parameter. Returns: Tensor: std value )rsqrtr4r+rrr)stddevs z Gumbel.stddevvaluecCsF|j||jj|j|jj}t|t||j|jS)zProbability density/mass function Args: value (Tensor): The input tensor. Returns: Tensor: probability.The data type is same with value. )r astyperr rexp)r&r7yrrr)probs "z Gumbel.probcCst||S)zLog probability density/mass function. Args: value (Tensor): The input tensor. Returns: Tensor: log probability.The data type is same with value. )rlogr;r&r7rrr)log_probs zGumbel.log_probcCs0tt||j|j |j|j S)zCumulative distribution function. Args: value (Tensor): value to be evaluated. Returns: Tensor: cumulative probability of value. )rr9r r8rr r=rrr)cdfs  z Gumbel.cdfcCst|jdtjS)z`Entropy of Gumbel distribution. Returns: Entropy of distribution. r)rr<r r r*r+rrr)entropyszGumbel.entropyr Sequence[int]cCs6t ||WdS1swYdS)zSample from ``Gumbel``. Args: shape (Sequence[int], optional): The sample shape. Defaults to []. Returns: Tensor: A tensor with prepended dimensions shape.The data type is float32. N)rZno_gradrsample)r&rrrr)samples $z Gumbel.samplec Csntj}tjtj|jjd|jjdt |j }tj|j|j }| | | | |j |S)areparameterized sample Args: shape (Sequence[int], optional): 1D `int32`. Shape of the generated samples. Defaults to []. Returns: Tensor: A tensor with prepended dimensions shape.The data type is float32. rr-)rr"Z ExpTransformZAffineTransformrr rr rZ ones_likeforwardZinverse_baserC)r&rZ exp_transZaffine_trans_1Zaffine_trans_2rrr)rB s"   zGumbel.rsample)r rr rrr)rr)r7rrr)rrArr)__name__ __module__ __qualname____doc____annotations__r%propertyr,r4r6r;r>r?r@rCrB __classcell__rrr'r)r "s& ;#        r ) __future__rr0rtypingrnumpyr rZ paddle.baserZ,paddle.distribution.transformed_distributionrcollections.abcrrZpaddle.distributionrr r rrrr)s